Field
Embodiments of the present disclosure generally relate to methods for determining processing chamber cleaning endpoints. More specifically, embodiments described herein relate to methods for in-situ etch rate determinations for chamber clean endpoint detection.
Description of the Related Art
Clean time is often a significant factor in semiconductor manufacturing processes and equipment productivity. Clean time generally refers to the amount of time required to clean a piece of manufacturing equipment. Cleaning processes are often performed periodically to increase the useful life of manufacturing equipment. Cleaning processes also reduce the probability of manufacturing defective microdevices as a result of sub-optimal processing environments within the manufacturing equipment. Accordingly, clean time associated with equipment cleaning has a relatively large impact on particle reduction and throughput efficiency.
Insufficient clean time may cause the undesirable accumulation of reactant products and byproducts within the manufacturing equipment which may result in increased device defectivity and process drift. Alternatively, excessive clean time can result in prolonged exposure to corrosive environments which may result in premature degradation of the manufacturing equipment components. In addition, excessive clean time has a generally negative impact on throughput.
Current endpoint detection methods for determining clean time generally involve monitoring a secondary radical or plasma signal. Conventional methods include the use of residual gas analysis (RGA), optical emission spectroscopy (OES), non-dispersive infrared spectroscopy (NDIR), etc. for endpoint determination. However, these methods may provide inaccurate endpoint determinations as a result of sub-optimal metrology conditions. For example, the lack of secondary plasma dissociation for RGA may result in inaccurate data for determining suitable endpoints. In another example, the lack of radicals/plasma at the detection location for OES may adversely affect the endpoint data. In addition, the instruments required to perform the aforementioned analysis may be prohibitively expensive and may not be compatible on all types of equipment where it is desirable to perform endpoint detection.
Therefore, what is needed in the art are improved methods for endpoint detection.